摘要
为了提高通信网络故障诊断精度,提出了一种基于改进极限学习机的通信网络故障诊断方法。采用CS算法对ELM进行优化,建立了基于CS-ELM的通信网络故障诊断模型。采用通信网络故障数据进行仿真分析,并与其他模型进行了对比分析。结果表明,CS-ELM模型在对测试集进行诊断时的正确率高达98.57%,诊断精度高于其他几种对比模型,验证了所提通信网络故障诊断方法的正确性。
In order to improve the accuracy of communication network fault diagnosis,a communication network fault diagnosis method based on an improved extreme learning machine is proposed.We optimized ELM using CS algorithm and established a communication network fault diagnosis model based on CS-ELM.Using communication network fault data for simulation analysis and comparing it with other models,the results show that the CS-ELM model has a high accuracy rate of 98.57%in diagnosing the test set,with higher diagnostic accuracy than other comparative models,verifying the correctness of the proposed communication network fault diagnosis method.
作者
屈新东
朱绍柯
潘叶
张郭
QU Xindong;ZHU Shaoke;PAN Ye;ZHANG Guo(China Mobile Communications Group Guangdong Co.,LTD,Guangzhou 510000,China)
出处
《智能计算机与应用》
2024年第9期131-135,共5页
Intelligent Computer and Applications
关键词
通信网络
故障诊断
极限学习机
布谷鸟搜索算法
communication network
fault diagnosis
extreme learning machine
cuckoo search algorithm
